Explore the vast range of titles available.

The global trend in manufacturing has shifted from a manufacturing-centric process toward a user-centric process. This has resulted in a shorter lifespan and a high product replacement rate of any consumer product. Germany has introduced the concept of Industry Revolution 4.0 (IR 4.0) to convert manufacturing processes and mechanisms into cyber-physical systems (CPS). Digital factory, being the first step into CPS and IR4.0, is being targeted as the most important evolution of the manufacturing industry. This paper defines digital factories and their differences between other similar domains such as smart factories, CPS, and virtual factories. The requirements and goals of a digital factory are explained in detail to facilitate future digital factory tool developments. Furthermore, the current challenges faced in the implementation of the digital factory are proposed to be approached by adapting an interoperable virtual reality technology. This paper emphasizes the usage of virtual reality (VR) in simulating a digital factory that aids in the decision-making and efficient operation of a manufacturing facility. Furthermore, recommendations gathered from previous studies for developing VR-based digital factory tools are also explained in detail in this paper.

PurposeIndustry 4.0 implies that global challenges exist within the manufacturing sector. Both theoretical and empirical research has been developed to support these transformations and assist companies in the process of changing. The purpose of this paper is to gather previous articles through an updated review and defines a research agenda for future investigation based on the most recent studies published in the field.Design/methodology/approachKey articles on the subject are analysed. The articles were published in 39 journals from which 107 papers dating from 2005 to 2018 have been selected.FindingsThe main findings imply the definition of a research agenda where: a common terminology should be created; the levels of implementation of Industry 4.0 should be defined; the stages of the development of Industry 4.0 should be identified; a lean approach for this industry is defined and the implications of Industry 4.0 in either a sustainable or circular economy should be understood; the consequences of human resources should be analysed; and the effects of the smart factory in the organisation are the areas identified and studied in the mentioned research agenda.Research limitations/implicationsThis review has some limitations. First, a number of grey literature, such as reports from non-governmental organisations and front-line practitioners’ reflections, were not included. Second, only research studies in English and Spanish were reviewed.Practical implicationsThis review helps practitioners in their implementation of Industry 4.0. Moreover, the identified future research areas may help to define priorities in this implementation.Originality/valueAfter examining previous research, this paper proposes a research agenda covering issues about Industry 4.0. This research agenda should guide future investigations in the smart industry.

The current study on digital factory (DF) meets some problems, such as disconnected manufacturing sites, independent digital models, isolated data, and non-self-controlled applications. In order to move current situation of DFs forward towards smart manufacturing, this paper attempts to present an overview of current digital situation of factories, and propose a systematical framework of cyber-physical integration in factories, with consideration of the concept of digital twin and the theory of manufacturing service. Particularly, the proposed framework includes four key issues, i.e., (a) fully interconnected physical elements integration , (b) faithful-mirrored virtual models integration , (c) all of elements/flows/businesses-covered data fusion , and (d) data-driven and application-oriented services integration . The corresponding implementable solutions of these four key issues are discussed in turn. As a reference, this paper is promising to bridge the gap in factories from current digital situation to smart manufacturing, so as to effectively facilitate their smart production.

In today’s industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events.

This article presents the results of research with the main goal of identifying possible applications of edge computing (EC) in industry. This study used the methodology of systematic literature review and text mining analysis. The main findings showed that the primary goal of EC is to reduce the time required to transfer large amounts of data. With the ability to analyze data at the edge, it is possible to obtain immediate feedback and use it in the decision-making process. However, the implementation of EC requires investments not only in infrastructure, but also in the development of employee knowledge related to modern computing methods based on artificial intelligence. As the results of the analyses showed, great importance is also attached to energy consumption, both in ongoing production processes and for the purposes of data transmission and analysis. This paper also highlights problems related to quality management. Based on the analyses, we indicate further research directions for the application of edge computing and associated technologies that are required in the area of intelligent resource scheduling (for flexible production systems and autonomous systems), anomaly detection and resulting decision making, data analysis and transfer, knowledge management (for smart designing), and simulations (for autonomous systems).

Purpose This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations. Design/methodology/approach This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques. Findings A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions. Originality/value Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Many enterprises have built their own digital twin factory model for physical factory planning, simulation optimization, and real-time monitoring. However, the digital twin system (DTS), which has a single domain, short time cycle, and unfulfillable services, cannot fully reflect the interaction and integration of the physical and informational world required by smart manufacturing. Therefore, research on the smart factory DTS (SFDTS) construction and application with cross-domain, multiple models have important influence on smart manufacturing. Given the above problems, this paper proposes the concept and composition of a digital twin manufacturing ecosystem (DTME) based on the requirements and characteristics of the product life cycle. It analyzes the construction requirements of the DTME for a factory DTS(FDTS), product DTS(PDTS), and supply chain DTS(SCDTS) from the perspective of the life cycle. Finally, the smart factory DTS architecture is applied to the digital and intelligent upgrading of the hydraulic cylinder factory. The experimental results reveal the intelligent improvement of the hydraulic factory, reduction of work-in-progress inventory, and advance of delivery time, proving the feasibility and effectiveness of the SFDTS.

The integration and application of a warehouse system and manufacturing system has become a manufacturing problem for enterprises. The main reason is that the information control system based on automation and stereo warehouse is inconsistent with the production and management information system of the enterprise in terms of business, data, functions, etc. Based on this, this paper studies the implementation of an automated warehouse based on the integration of ERP (enterprise resource planning) and WMS (warehouse management system) with the method and technology of the intermediate table. Moreover, MES (manufacturing execution system) is the brain and the core part of a sustainable digital factory. The enterprise adopts advanced intelligent and information technology to build and deploy the MES, realize fine management and agile production, and meet the personalized needs of the market. Therefore, this paper studies the implementation path and effect based on MES from an industrial realization to construct a sustainable digital factory. The research results of this paper can improve industrial efficiency and reduce costs for enterprises in storage capacity, handling capacity, response rate, rate of error, number of operators, etc.

The combination of distributed digital factories (D2Fs) with sustainable practices has been proposed as a revolutionary technique in modern manufacturing. This review paper explores the convergence of D2F with innovative sensor technology, concentrating on the role of Field Programmable Gate Arrays (FPGAs) in promoting this paradigm. A D2F is defined as an integrated framework where digital twins (DTs), sensors, laser additive manufacturing (laser-AM), and subtractive manufacturing (SM) work in synchronization. Here, DTs serve as a virtual replica of physical machines, allowing accurate monitoring and control of a given manufacturing process. These DTs are supplemented by sensors, providing near-real-time data to assure the effectiveness of the manufacturing processes. FPGAs, identified for their re-programmability, reduced power usage, and enhanced processing compared to traditional processors, are increasingly being used to develop near-real-time monitoring systems within manufacturing networks. This review paper identifies the recent expansions in FPGA-based sensors and their exploration within the D2Fs operations. The primary topics incorporate the deployment of eco-efficient data management and near-real-time monitoring, targeted at lowering waste and optimizing resources. The review paper also identifies the future research directions in this field. By incorporating advanced sensors, DTs, laser-AM, and SM processes, this review emphasizes a path toward more sustainable and resilient D2Fs operations.

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.